NDVI Response to Satellite-Estimated Antecedent Precipitation in Dryland Pastures

Precipitation is a critical driver of vegetation productivity and dynamics in dryland environments, especially areas with intense livestock farming. Availability access to accurate, reliable, timely rainfall data are essential for natural resources management, environmental monitoring, informing hydrological rainfall-runoff models. Gauged precipitation drylands often scarce, fragmented, low spatial resolution; therefore, satellite-estimated becomes valuable dataset overcoming this constraint. Using statistical indices, we compared satellite-derived from four products (CHIRPS, GPM, TRMM, PERSIANN-CDR) against gauged at different temporal scales (daily, monthly, yearly). Spatial correlations were calculated GPM CHIRPS estimates interpolated precipitation. We then estimated NDVI response Antecedent Accumulated (AAP) 1, 3, 6, 9, 12 months major types typical the region. Statistical metrics varied being highest acceptable periods 1 month or year. At monthly scale presented best Pearson’s Correlation Coefficient (r), Root Mean Square Error (RMSE) RMSE-observations standard deviation ratio (RSR) resulted lower (ME) Bias. On an annual basis showed adjustment all indicators except r. responses 3 AAP significant study area. The findings show that satellites accurate as tool analysing relationships between Mendoza.